---
title: ChatLiteLLM and ChatLiteLLMRouter
---

[LiteLLM](https://github.com/BerriAI/litellm) is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc.

This notebook covers how to get started with using LangChain + the LiteLLM I/O library.

This integration contains two main classes:

- `ChatLiteLLM`: The main LangChain wrapper for basic usage of LiteLLM ([docs](https://docs.litellm.ai/docs/)).
- `ChatLiteLLMRouter`: A `ChatLiteLLM` wrapper that leverages LiteLLM's Router ([docs](https://docs.litellm.ai/docs/routing)).

## Table of Contents

1. [Overview](#overview)
   - [Integration Details](#integration-details)
   - [Model Features](#model-features)
2. [Setup](#setup)
3. [Credentials](#credentials)
4. [Installation](#installation)
5. [Instantiation](#instantiation)
   - [ChatLiteLLM](#chatlitellm)
   - [ChatLiteLLMRouter](#chatlitellmrouter)
6. [Invocation](#invocation)
7. [Async and Streaming Functionality](#async-and-streaming-functionality)
8. [API Reference](#api-reference)

## Overview

### Integration details

| Class | Package | Local | Serializable | JS support| Downloads | Version |
| :---  | :--- | :---: | :---: |  :---: | :---: | :---: |
| [ChatLiteLLM](https://python.langchain.com/docs/integrations/chat/litellm/#chatlitellm) | [langchain-litellm](https://pypi.org/project/langchain-litellm/)| ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-litellm?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-litellm?style=flat-square&label=%20) |
| [ChatLiteLLMRouter](https://python.langchain.com/docs/integrations/chat/litellm/#chatlitellmrouter) | [langchain-litellm](https://pypi.org/project/langchain-litellm/)| ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-litellm?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-litellm?style=flat-square&label=%20) |

### Model features

| [Tool calling](https://python.langchain.com/docs/how_to/tool_calling/) | [Structured output](https://python.langchain.com/docs/how_to/structured_output/) | JSON mode | Image input | Audio input | Video input | [Token-level streaming](https://python.langchain.com/docs/integrations/chat/litellm/#chatlitellm-also-supports-async-and-streaming-functionality) | [Native async](https://python.langchain.com/docs/integrations/chat/litellm/#chatlitellm-also-supports-async-and-streaming-functionality) | [Token usage](https://python.langchain.com/docs/how_to/chat_token_usage_tracking/) | [Logprobs](https://python.langchain.com/docs/how_to/logprobs/) |
| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |
| ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ |

### Setup

To access `ChatLiteLLM` and `ChatLiteLLMRouter` models, you'll need to install the `langchain-litellm` package and create an OpenAI, Anthropic, Azure, Replicate, OpenRouter, Hugging Face, Together AI, or Cohere account. Then, you have to get an API key and export it as an environment variable.

## Credentials

You have to choose the LLM provider you want and sign up with them to get their API key.

### Example - Anthropic

Head to [console.anthropic.com/](https://console.anthropic.com/) to sign up for Anthropic and generate an API key. Once you've done this, set the ANTHROPIC_API_KEY environment variable.

### Example - OpenAI

Head to [platform.openai.com/api-keys](https://platform.openai.com/api-keys) to sign up for OpenAI and generate an API key. Once you've done this, set the OPENAI_API_KEY environment variable.

```python
## Set ENV variables
import os

os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key"
```

### Installation

The LangChain LiteLLM integration is available in the `langchain-litellm` package:

```python
%pip install -qU langchain-litellm
```

## Instantiation

### ChatLiteLLM

You can instantiate a `ChatLiteLLM` model by providing a `model` name [supported by LiteLLM](https://docs.litellm.ai/docs/providers).

```python
from langchain_litellm import ChatLiteLLM

llm = ChatLiteLLM(model="gpt-4.1-nano", temperature=0.1)
```

### ChatLiteLLMRouter

You can also leverage LiteLLM's routing capabilities by defining your model list as specified [here](https://docs.litellm.ai/docs/routing).

```python
from langchain_litellm import ChatLiteLLMRouter
from litellm import Router

model_list = [
    {
        "model_name": "gpt-4.1",
        "litellm_params": {
            "model": "azure/gpt-4.1",
            "api_key": "<your-api-key>",
            "api_version": "2024-10-21",
            "api_base": "https://<your-endpoint>.openai.azure.com/",
        },
    },
    {
        "model_name": "gpt-4o",
        "litellm_params": {
            "model": "azure/gpt-4o",
            "api_key": "<your-api-key>",
            "api_version": "2024-10-21",
            "api_base": "https://<your-endpoint>.openai.azure.com/",
        },
    },
]
litellm_router = Router(model_list=model_list)
llm = ChatLiteLLMRouter(router=litellm_router, model_name="gpt-4.1", temperature=0.1)
```

## Invocation

Whether you've instantiated a `ChatLiteLLM` or a `ChatLiteLLMRouter`, you can now use the ChatModel through LangChain's API.

```python
response = await llm.ainvoke(
    "Classify the text into neutral, negative or positive. Text: I think the food was okay. Sentiment:"
)
print(response)
```

```output
content='Neutral' additional_kwargs={} response_metadata={'token_usage': Usage(completion_tokens=2, prompt_tokens=30, total_tokens=32, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0, text_tokens=None), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=0, cached_tokens=0, text_tokens=None, image_tokens=None)), 'model': 'gpt-3.5-turbo', 'finish_reason': 'stop', 'model_name': 'gpt-3.5-turbo'} id='run-ab6a3b21-eae8-4c27-acb2-add65a38221a-0' usage_metadata={'input_tokens': 30, 'output_tokens': 2, 'total_tokens': 32}
```

## Async and Streaming Functionality

`ChatLiteLLM` and `ChatLiteLLMRouter` also support async and streaming functionality:

```python
async for token in llm.astream("Hello, please explain how antibiotics work"):
    print(token.text(), end="")
```

```output
Antibiotics are medications that fight bacterial infections in the body. They work by targeting specific bacteria and either killing them or preventing their growth and reproduction.

There are several different mechanisms by which antibiotics work. Some antibiotics work by disrupting the cell walls of bacteria, causing them to burst and die. Others interfere with the protein synthesis of bacteria, preventing them from growing and reproducing. Some antibiotics target the DNA or RNA of bacteria, disrupting their ability to replicate.

It is important to note that antibiotics only work against bacterial infections and not viral infections. It is also crucial to take antibiotics as prescribed by a healthcare professional and to complete the full course of treatment, even if symptoms improve before the medication is finished. This helps to prevent antibiotic resistance, where bacteria become resistant to the effects of antibiotics.
```

## API reference

For detailed documentation of all `ChatLiteLLM` and `ChatLiteLLMRouter` features and configurations, head to the API reference: [github.com/Akshay-Dongare/langchain-litellm](https://github.com/Akshay-Dongare/langchain-litellm)
